Audio_reactive_Latent_Interpolations_with_StyleGAN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Audio-reactive Latent Interpolations with StyleGAN",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/maxemitchell/colab-notebooks/blob/main/Audio_reactive_Latent_Interpolations_with_StyleGAN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6j6CYasTcvIM"
      },
      "source": [
        "This notebook shows a simple workflow to render audio-reactive latent interpolations as described in [this paper](https://wavefunk.xyz/assets/audio-reactive-stylegan/paper.pdf) for the NeurIPS 2020 [Workshop on Machine Learning for Creativity & Design](https://neurips2020creativity.github.io/).\n",
        "\n",
        "A very basic, WIP notebook to choose latent vectors for the interpolations can be found [here](https://colab.research.google.com/drive/1BJ3wk_SkCftjLF2jJXOt0OsiETy1F1bz) (for now it's only verified to work for Linux, as it requires forwarding X11 to an SSH session from your local machine).\n",
        "\n",
        "You can find a slightly simpler description of the paper's main contributions in this [blog post](https://wavefunk.xyz/audio-reactive-stylegan)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KnFLsJZk-15k"
      },
      "source": [
        "# Preparation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yhJVWUx-69m8"
      },
      "source": [
        "Upload an audio file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "0RvpNwlAOHo0",
        "outputId": "5c4277eb-716a-4588-b8a0-1af3b60ed358"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "audio_file = list(uploaded.keys())[0]\n",
        "print('User uploaded file \"{name}\" with length {length} bytes'.format(name=audio_file, length=len(uploaded[audio_file])))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-e4001840-2e79-46cd-bd1a-3b265b751cb0\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-e4001840-2e79-46cd-bd1a-3b265b751cb0\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving Song 113.d.wav to Song 113.d.wav\n",
            "User uploaded file \"Song 113.d.wav\" with length 32212220 bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QZ6NXPoR7KbQ"
      },
      "source": [
        "Download all the required dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CvFRN3PjXaiM",
        "outputId": "2278a684-fb5a-402d-873c-369e41b31cc7"
      },
      "source": [
        "!nvidia-smi --query-gpu=name,memory.free --format=csv,noheader\n",
        "!echo\n",
        "%tensorflow_version 1.x\n",
        "!git clone https://github.com/NVlabs/stylegan2.git\n",
        "!git clone https://github.com/NVlabs/stylegan2-ada.git\n",
        "!git clone https://github.com/rosinality/stylegan2-pytorch\n",
        "!git clone https://github.com/JCBrouwer/maua-stylegan2\n",
        "!mv \"$audio_file\" maua-stylegan2/\n",
        "%cd /content/maua-stylegan2\n",
        "!pip install librosa madmom kornia Ninja matplotlib scikit-image gdown\n",
        "%matplotlib inline\n",
        "!wget https://github.com/kkroening/ffmpeg-python/archive/0.2.0.zip\n",
        "!unzip 0.2.0.zip\n",
        "!mv ffmpeg-python-0.2.0/ffmpeg ./\n",
        "!rm -rf ffmpeg-python-0.2.0/ 0.2.0.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tesla P100-PCIE-16GB, 16280 MiB\n",
            "\n",
            "TensorFlow 1.x selected.\n",
            "fatal: destination path 'stylegan2' already exists and is not an empty directory.\n",
            "fatal: destination path 'stylegan2-ada' already exists and is not an empty directory.\n",
            "fatal: destination path 'stylegan2-pytorch' already exists and is not an empty directory.\n",
            "fatal: destination path 'maua-stylegan2' already exists and is not an empty directory.\n",
            "mv: cannot stat '': No such file or directory\n",
            "/content/maua-stylegan2\n",
            "Requirement already satisfied: librosa in /usr/local/lib/python3.7/dist-packages (0.8.0)\n",
            "Requirement already satisfied: madmom in /usr/local/lib/python3.7/dist-packages (0.16.1)\n",
            "Requirement already satisfied: kornia in /usr/local/lib/python3.7/dist-packages (0.5.0)\n",
            "Requirement already satisfied: Ninja in /usr/local/lib/python3.7/dist-packages (1.10.0.post2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (3.2.2)\n",
            "Requirement already satisfied: scikit-image in /usr/local/lib/python3.7/dist-packages (0.16.2)\n",
            "Requirement already satisfied: gdown in /usr/local/lib/python3.7/dist-packages (3.6.4)\n",
            "Requirement already satisfied: audioread>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from librosa) (2.1.9)\n",
            "Requirement already satisfied: pooch>=1.0 in /usr/local/lib/python3.7/dist-packages (from librosa) (1.3.0)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from librosa) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from librosa) (1.19.5)\n",
            "Requirement already satisfied: soundfile>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from librosa) (0.10.3.post1)\n",
            "Requirement already satisfied: joblib>=0.14 in /usr/local/lib/python3.7/dist-packages (from librosa) (1.0.1)\n",
            "Requirement already satisfied: resampy>=0.2.2 in /usr/local/lib/python3.7/dist-packages (from librosa) (0.2.2)\n",
            "Requirement already satisfied: decorator>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from librosa) (4.4.2)\n",
            "Requirement already satisfied: numba>=0.43.0 in /usr/local/lib/python3.7/dist-packages (from librosa) (0.51.2)\n",
            "Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from librosa) (0.22.2.post1)\n",
            "Requirement already satisfied: mido>=1.2.8 in /usr/local/lib/python3.7/dist-packages (from madmom) (1.2.9)\n",
            "Requirement already satisfied: cython>=0.25 in /usr/local/lib/python3.7/dist-packages (from madmom) (0.29.22)\n",
            "Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from kornia) (1.8.1+cu101)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.3.1)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (2.8.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (2.4.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (0.10.0)\n",
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          ],
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AidonrsL7N0c"
      },
      "source": [
        "Download a stylegan network checkpoint (alternatively upload your own, drag and drop to files menu on left).\n",
        "\n",
        "good places to find networks:\n",
        "\n",
        "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/\n",
        "\n",
        "https://github.com/justinpinkney/awesome-pretrained-stylegan2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kcmbcdTDNkEQ",
        "outputId": "f2d5b9d5-4f5d-4ca1-83cc-b3c38ee45815"
      },
      "source": [
        "# FFHQ\n",
        "# !wget https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/ffhq.pkl\n",
        "\n",
        "# Derrick Schultz's Frea Buckler network (from awesome-pretrained-stylegan2)\n",
        "!gdown https://drive.google.com/u/0/uc?id=1YzZemZAp7BVW701_BZ7uabJWJJaS2g7v"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/u/0/uc?id=10ipb7kGmkaVI3FFh_k3MoMlla--NCq1Y\n",
            "To: /content/maua-stylegan2/network-snapshot-000384.pkl\n",
            "382MB [00:04, 85.3MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bmCAwJdU7X84"
      },
      "source": [
        "Convert tensorflow .pkl checkpoint to pytorch .pt checkpoint\n",
        "\n",
        "`--repo` argument must coincide with the repository that the .pkl was trained with"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fqdlX27DP82Y",
        "outputId": "e23bb5ed-f847-4e89-f6d8-c04e73a79e1a"
      },
      "source": [
        "# !python ../stylegan2-pytorch/convert_weight.py --gen ffhq.pkl --repo ../stylegan2-ada/\n",
        "# !python ../stylegan2-pytorch/convert_weight.py --gen FreaGAN.pkl --repo ../stylegan2/\n",
        "!python ../stylegan2-pytorch/convert_weight.py --gen network-snapshot-000384.pkl --repo ../stylegan2-ada/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Setting up TensorFlow plugin \"fused_bias_act.cu\": Compiling... Loading... Done.\n",
            "Setting up TensorFlow plugin \"upfirdn_2d.cu\": Compiling... Loading... Done.\n",
            "tensor(0.1432, device='cuda:0')\n",
            "/usr/local/lib/python3.7/dist-packages/torchvision/utils.py:50: UserWarning: range will be deprecated, please use value_range instead.\n",
            "  warnings.warn(warning)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F4pe-cKI-8ls"
      },
      "source": [
        "# Audio-reactive"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DcGAgmOj7p2M"
      },
      "source": [
        "This is where the functions that make the video react to the music are defined.\n",
        "\n",
        "You can find more examples of these functions [here](https://github.com/JCBrouwer/maua-stylegan2/tree/master/audioreactive/examples)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gzl78Pv6_KBE"
      },
      "source": [
        "\n",
        "First initialize things that will be resused multiple times\n",
        "\n",
        "n.b. get_noise() will be called >10 times for different sizes, so preparing things beforehand can save a lot of *time*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kW8-8b6VJf3I"
      },
      "source": [
        "import torch as th\n",
        "import librosa as rosa\n",
        "import audioreactive as ar\n",
        "\n",
        "def initialize(args):\n",
        "    # exercise for the reader:\n",
        "    # install https://github.com/deezer/spleeter\n",
        "    # split your audio file into 4 tracks\n",
        "    # then load them individually for higher-quality onsets/chroma/etc. e.g.:\n",
        "    #     !spleeter separate $audio_file -p spleeter:4stems\n",
        "    #     drums, drum_sr = rosa.load(\"/path/to/drum_file.wav\")\n",
        "\n",
        "    # args.lo_onsets = ar.onsets(args.audio, args.sr, args.n_frames, fmax=150, smooth=3)\n",
        "    # args.hi_onsets = ar.onsets(args.audio, args.sr, args.n_frames, fmin=150, smooth=3)\n",
        "\n",
        "    rms = ar.rms(args.audio, args.sr, args.n_frames, smooth=30, clip=50, power=1)\n",
        "    rms = ar.expand(rms, threshold=0.8, ratio=10)\n",
        "    rms = ar.gaussian_filter(rms, 4)\n",
        "    rms = ar.normalize(rms)\n",
        "    args.rms = rms\n",
        "\n",
        "    # cheating a little here, this my song so I have the multitracks\n",
        "    # this is much easier than fiddling with onsets until you have envelopes that dance nicely to the drums\n",
        "    audio, sr = rosa.load(\"/content/maua-stylegan2/Song 113.drums.wav\", offset=args.offset, duration=args.duration)\n",
        "    args.drum_onsets = ar.onsets(audio, sr, args.n_frames, margin=12, smooth=10)\n",
        "    audio, sr = rosa.load(\"/content/maua-stylegan2/Song 113.hi.wav\", offset=args.offset, duration=args.duration)\n",
        "    args.hi_onsets = ar.onsets(audio, sr, args.n_frames, margin=1, smooth=10)\n",
        "\n",
        "    ar.plot_signals([args.rms, args.drum_onsets, args.hi_onsets])\n",
        "\n",
        "    # print(\"onsets:\")\n",
        "    # ar.plot_signals([args.hi_onsets, args.lo_onsets])\n",
        "\n",
        "    return args"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YvllOPS48AW0"
      },
      "source": [
        "Create audioreactive latents default shape for 1024px network is: [n_frames, 18, 512]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9-U6a7SjJi1w"
      },
      "source": [
        "BPM = 92\n",
        "\n",
        "def get_latents(selection, args):\n",
        "    chroma = ar.chroma(args.audio, args.sr, args.n_frames)\n",
        "\n",
        "    print(\"chroma:\")\n",
        "    ar.plot_spectra([chroma], chroma=True)\n",
        "\n",
        "    chroma_latents = ar.chroma_weight_latents(chroma, selection).float()\n",
        "    # print(chroma_latents, chroma_latents.shape)\n",
        "    latents = ar.gaussian_filter(chroma_latents.float(), 4)\n",
        "\n",
        "    lo_onsets = args.drum_onsets[:, None, None]\n",
        "    hi_onsets = args.hi_onsets[:, None, None]\n",
        "\n",
        "    latents = hi_onsets * selection[[-4]] + (1 - hi_onsets) * latents\n",
        "    latents = lo_onsets * selection[[-7]] + (1 - lo_onsets) * latents\n",
        "\n",
        "    latents = ar.gaussian_filter(latents, 2.0, causal=0.2)\n",
        "\n",
        "    return latents\n",
        "\n",
        "    # expand envelopes to latent shape\n",
        "    # rms = args.rms[:, None, None]\n",
        "    # low_onsets = args.drum_onsets[:, None, None]\n",
        "    # high_onsets = args.hi_onsets[:, None, None]\n",
        "\n",
        "    # # get timestamps and labels with laplacian segmentation\n",
        "    # # k is the number of labels the algorithm may use\n",
        "    # # try multiple values with plot=True to see which value correlates best with the sections of the song\n",
        "    # #MM 4 for 60s, 10 for entire song\n",
        "    # timestamps, labels = ar.laplacian_segmentation(args.audio, args.sr, k=10, plot=True)\n",
        "\n",
        "    # # a second set of latents for the drop section, the 'selection' variable is the other set for the intro\n",
        "    # drop_selection = ar.load_latents(\"/content/maua-stylegan2/my_latents.npy\")\n",
        "    # # MM 10 is abstract, \n",
        "    # color_layer = 3 ## MM might need to experiment w this?\n",
        "\n",
        "    # latents = []\n",
        "    # for (start, stop), l in zip(zip(timestamps, timestamps[1:]), labels):\n",
        "    #     start_frame = int(round(start / args.duration * args.n_frames))\n",
        "    #     stop_frame = int(round(stop / args.duration * args.n_frames))\n",
        "    #     section_frames = stop_frame - start_frame\n",
        "    #     section_bars = (stop - start) * (BPM / 60) / 4\n",
        "\n",
        "    #     # get portion of latent selection (wrapping around to start)\n",
        "    #     latent_selection_slice = ar.wrapping_slice(selection, l, 4)\n",
        "    #     # spline interpolation loops through selection slice\n",
        "    #     latent_section = ar.spline_loops(latent_selection_slice, n_frames=section_frames, n_loops=section_bars / 4)\n",
        "    #     # set the color with laplacian segmentation label, (1 latent repeated for entire section in upper layers)\n",
        "    #     latent_section[:, color_layer:] = th.cat([selection[[l], color_layer:]] * section_frames)\n",
        "\n",
        "    #     # same as above but for the drop latents (with faster loops)\n",
        "    #     drop_selection_slice = ar.wrapping_slice(drop_selection, l, 4)\n",
        "    #     drop_section = ar.spline_loops(drop_selection_slice, n_frames=section_frames, n_loops=section_bars / 2)\n",
        "    #     drop_section[:, color_layer:] = th.cat([drop_selection[[l], color_layer:]] * section_frames)\n",
        "\n",
        "    #     # merged based on RMS (drop section or not)\n",
        "    #     latents.append((1 - rms[start_frame:stop_frame]) * latent_section + rms[start_frame:stop_frame] * drop_section)\n",
        "\n",
        "    # # concatenate latents to correct length & smooth over the junctions\n",
        "    # len_latents = sum([len(l) for l in latents])\n",
        "    # if len_latents != args.n_frames:\n",
        "    #     latents.append(th.cat([latents[-1][[-1]]] * (args.n_frames - len_latents)))\n",
        "    # latents = th.cat(latents).float()\n",
        "    # latents = ar.gaussian_filter(latents, 3)\n",
        "\n",
        "    # # use onsets to modulate towards latents\n",
        "    # latents = 0.9 * low_onsets * selection[[2]] + (1 - 0.9 * low_onsets) * latents\n",
        "    # latents = 0.9 * high_onsets * selection[[1]] + (1 - 0.9 * high_onsets) * latents\n",
        "\n",
        "    # latents = ar.gaussian_filter(latents, 1, causal=0.2)\n",
        "    # return latents\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CEYj-kwz8OOU"
      },
      "source": [
        "Create audioreactive noise"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Zfh-nHnJlA0"
      },
      "source": [
        "import scipy.ndimage.filters as ndi\n",
        "import numpy as np\n",
        "\n",
        "# def get_noise(height, width, scale, num_scales, args):\n",
        "#     if width > 256:\n",
        "#         return None\n",
        "\n",
        "#     lo_onsets = args.drum_onsets[:, None, None, None].cuda()\n",
        "#     hi_onsets = args.hi_onsets[:, None, None, None].cuda()\n",
        "\n",
        "#     noise_noisy = ar.gaussian_filter(th.randn((args.n_frames, 1, height, width), device=\"cuda\"), 5)\n",
        "#     noise = ar.gaussian_filter(th.randn((args.n_frames, 1, height, width), device=\"cuda\"), 128)\n",
        "\n",
        "#     if width < 128:\n",
        "#         noise = lo_onsets * noise_noisy + (1 - lo_onsets) * noise\n",
        "#     if width > 32:\n",
        "#         noise = hi_onsets * noise_noisy + (1 - hi_onsets) * noise\n",
        "\n",
        "#     noise /= noise.std() * 2.5\n",
        "\n",
        "#     return noise.cpu()\n",
        "\n",
        "def circular_mask(h, w, center=None, radius=None, soft=0):\n",
        "    if center is None:  # use the middle of the image\n",
        "        center = (int(w / 2), int(h / 2))\n",
        "    if radius is None:  # use the smallest distance between the center and image walls\n",
        "        radius = min(center[0], center[1], w - center[0], h - center[1])\n",
        "\n",
        "    Y, X = np.ogrid[:h, :w]\n",
        "    dist_from_center = np.sqrt((X - center[0]) ** 2 + (Y - center[1]) ** 2)\n",
        "    mask = dist_from_center <= radius\n",
        "\n",
        "    if soft > 0:\n",
        "        mask = ndi.gaussian_filter(mask, sigma=int(round(soft)))  # blur mask for smoother transition\n",
        "\n",
        "    return th.from_numpy(mask)\n",
        "\n",
        "\n",
        "def get_noise(height, width, scale, num_scales, args):\n",
        "    if width > 256:  # larger sizes don't fit in VRAM, just use default or randomize\n",
        "        return None\n",
        "\n",
        "    # expand onsets to noise shape\n",
        "    # send to GPU as gaussian_filter on large noise tensors with high standard deviation is slow\n",
        "    lo_onsets = args.drum_onsets[:, None, None, None].cuda()\n",
        "    hi_onsets = args.hi_onsets[:, None, None, None].cuda()\n",
        "\n",
        "    # 1s inside circle of radius, 0s outside\n",
        "    mask = circular_mask(height, width, radius=int(width / 2), soft=2)[None, None, ...].float().cuda()\n",
        "\n",
        "    # create noise which changes quickly (small standard deviation smoothing)\n",
        "    noise_noisy = ar.gaussian_filter(th.randn((args.n_frames, 1, height, width), device=\"cuda\"), 5)\n",
        "\n",
        "    # create noise which changes slowly (large standard deviation smoothing)\n",
        "    noise = ar.gaussian_filter(th.randn((args.n_frames, 1, height, width), device=\"cuda\"), 128)\n",
        "\n",
        "    # for lower layers, noise inside circle are affected by low onsets\n",
        "    if width < 128:\n",
        "        noise = 2 * mask * lo_onsets * noise_noisy + (1 - mask) * (1 - lo_onsets) * noise\n",
        "    # for upper layers, noise outside circle are affected by high onsets\n",
        "    if width > 32:\n",
        "        noise = 0.75 * (1 - mask) * hi_onsets * noise_noisy + mask * (1 - 0.75 * hi_onsets) * noise\n",
        "\n",
        "    # ensure amplitude of noise is close to standard normal distribution (dividing by std. dev. gets it exactly there)\n",
        "    noise /= noise.std() * 2\n",
        "\n",
        "    return noise.cpu()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oF53jKmR8TZk"
      },
      "source": [
        "Create audioreactive network bends (here the width is doubled for 1920x1080 output)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fOde375CrLZ0"
      },
      "source": [
        "import math\n",
        "\n",
        "import kornia.augmentation as kA\n",
        "import kornia.geometry.transform as kT\n",
        "from functools import partial\n",
        "\n",
        "class NetworkBend(th.nn.Module):\n",
        "    \"\"\"Base network bending class\n",
        "    Args:\n",
        "        sequential_fn (function): Function that takes a batch of modulation and creates th.nn.Sequential\n",
        "        modulation (th.tensor): Modulation batch\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, sequential_fn, modulation):\n",
        "        super(NetworkBend, self).__init__()\n",
        "        self.sequential = sequential_fn(modulation)\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.sequential(x)\n",
        "\n",
        "class AddNoise(th.nn.Module):\n",
        "    \"\"\"Adds static noise to output\n",
        "    Args:\n",
        "        noise (th.tensor): Noise to be added\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, noise):\n",
        "        super(AddNoise, self).__init__()\n",
        "        self.noise = noise\n",
        "\n",
        "    def forward(self, x):\n",
        "        return x + self.noise.to(x.device)\n",
        "\n",
        "# class Translate(NetworkBend):\n",
        "#     \"\"\"Creates horizontal translating effect where repeated linear interpolations from 0 to 1 (saw tooth wave) creates seamless scrolling effect.\n",
        "#     Args:\n",
        "#         modulation (th.tensor): [0.0-1.0]. Batch of modulation\n",
        "#         h (int): Height of intermediate features that the network bend is applied to\n",
        "#         w (int): Width of intermediate features that the network bend is applied to\n",
        "#         noise (int): Noise to be added (must be 5 * width wide)\n",
        "#     \"\"\"\n",
        "\n",
        "#     def __init__(self, modulation, h, w, noise):\n",
        "#         sequential_fn = lambda b: th.nn.Sequential(\n",
        "#             th.nn.ReflectionPad2d((int(w / 2), int(w / 2), 0, 0)),\n",
        "#             th.nn.ReflectionPad2d((w, w, 0, 0)),\n",
        "#             th.nn.ReflectionPad2d((w, 0, 0, 0)),\n",
        "#             AddNoise(noise),\n",
        "#             kT.Translate(b),\n",
        "#             kA.CenterCrop((h, w)),\n",
        "#         )\n",
        "#         super(Translate, self).__init__(sequential_fn, modulation)\n",
        "\n",
        "# class TranslateDown(NetworkBend):\n",
        "#     def __init__(self, modulation, h, w, noise):\n",
        "#         sequential_fn = lambda b: th.nn.Sequential(\n",
        "#             th.nn.ReflectionPad2d((0, 0, int(h / 2), int(h / 2))),\n",
        "#             th.nn.ReflectionPad2d((0, 0, h, h)),\n",
        "#             th.nn.ReflectionPad2d((0, 0, h, 0)),\n",
        "#             AddNoise(noise),\n",
        "#             kT.Translate(b),\n",
        "#             kA.CenterCrop((h, w)),\n",
        "#         )\n",
        "#         super(TranslateDown, self).__init__(sequential_fn, modulation)\n",
        "\n",
        "class TranslateBoth(NetworkBend):\n",
        "    def __init__(self, modulation, h, w, noise):\n",
        "        sequential_fn = lambda b: th.nn.Sequential(\n",
        "            th.nn.ReflectionPad2d((int(w / 2), int(w / 2), int(h / 2), int(h / 2))),\n",
        "            th.nn.ReflectionPad2d((w, w, h, h)),\n",
        "            th.nn.ReflectionPad2d((w, 0, h, 0)),\n",
        "            AddNoise(noise),\n",
        "            kT.Translate(b),\n",
        "            kA.CenterCrop((h, w)),\n",
        "        )\n",
        "        super(TranslateBoth, self).__init__(sequential_fn, modulation)\n",
        "\n",
        "class Rotate(NetworkBend):\n",
        "    \"\"\"Creates rotation effect.\n",
        "    Args:\n",
        "        modulation (th.tensor): [0.0-1.0]. Batch of modulation\n",
        "        h (int): height of intermediate features that the network bend is applied to\n",
        "        w (int): width of intermediate features that the network bend is applied to\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, modulation, h, w):\n",
        "        # worst case rotation brings sqrt(2) * max_side_length out-of-frame pixels into frame\n",
        "        # padding should cover that exactly\n",
        "        padding = int(max(h, w) * (1 - math.sqrt(2) / 2))\n",
        "        sequential_fn = lambda b: th.nn.Sequential(th.nn.ReflectionPad2d(padding), kT.Rotate(b), kA.CenterCrop((h, w)))\n",
        "        super(Rotate, self).__init__(sequential_fn, modulation)\n",
        "        \n",
        "def get_bends(args):\n",
        "    transform = th.nn.Sequential(\n",
        "        th.nn.ReplicationPad2d((2, 2, 0, 0)), ar.AddNoise(0.001 * th.randn(size=(1, 1, 4, 8), device=\"cuda\")),\n",
        "    )\n",
        "\n",
        "    bends = [{\"layer\": 0, \"transform\": transform}]\n",
        "    # Layer 2 - choppy, layer 4 - normal, 6 - nchoppy \n",
        "\n",
        "    t1 = 5\n",
        "    h = 16 # 16 or 64 for 5\n",
        "    w = 2 * h\n",
        "\n",
        "    magic = args.fps * args.duration\n",
        "    # 5478 20-41 (right), 41-83(spin), 104-125(down), 125- 145(up), 145-166 (energetic), 166-end(zoom out?)\n",
        "    right_start = int(magic * (20 / args.duration))\n",
        "    right_end = int(magic * (41 / args.duration))\n",
        "    spin_start = int(magic * (41 / args.duration))\n",
        "    spin_end = int(magic * (83 / args.duration))\n",
        "    down_start = int(magic * (104 / args.duration))\n",
        "    down_end = int(magic * (125 / args.duration))\n",
        "    up_start = int(magic * (125 / args.duration))\n",
        "    up_end = int(magic * (145 / args.duration))\n",
        "    energetic_start = int(magic * (145 / args.duration))\n",
        "    energetic_end = int(magic * (166 / args.duration))\n",
        "\n",
        "    loop_length = int(10 * args.fps)\n",
        "  \n",
        "    up_loop_num = int((up_end - up_start) / loop_length)\n",
        "    up_trunc = (up_end - up_start) - up_loop_num * loop_length\n",
        "    energetic_loop_num = int((right_end - energetic_start) / loop_length)\n",
        "    energetic_trunc = (energetic_end - energetic_start) - energetic_loop_num * loop_length\n",
        "\n",
        "    # right_loop_length = int((right_end - right_start) / 3)\n",
        "\n",
        "    # intro_right = np.zeros(right_start)\n",
        "    # loops_right = np.concatenate([np.linspace(0, w, right_loop_length)] * 3)\n",
        "    # outro_right = np.zeros(args.n_frames - right_end)\n",
        "\n",
        "    # down_loop_length = int((down_end - down_start) / 3)\n",
        "    # intro_down = np.zeros(down_start)\n",
        "    # loops_down = np.concatenate([np.linspace(0, h, int(down_loop_length/2)), np.linspace(h, 0, int(down_loop_length/2))] * 3)\n",
        "    # outro_down = np.zeros(args.n_frames - down_end)\n",
        "    \n",
        "    # y_down = np.concatenate([intro_down, loops_down, outro_down])\n",
        "    \n",
        "    # x_right = np.concatenate([intro_right, loops_right, outro_right])\n",
        "    # x_right += (w/2.0)\n",
        "    # x_right = x_right % (w*1.0)\n",
        "    # y_right = np.zeros(args.n_frames)\n",
        "    # y_right = np.concatenate([intro_down, loops_down, outro_down])\n",
        "    # y_right += (h/2.0)\n",
        "    # y_right = y_right % (h*1.0)\n",
        "    # right_translation = (th.tensor([x_right, y_right]).float().T)[: args.n_frames]\n",
        "    # right_translation.T[0, right_start - args.fps : right_start + args.fps] = ar.gaussian_filter(\n",
        "    #     right_translation.T[0, right_start - 5 * args.fps : right_start + 5 * args.fps], 5\n",
        "    # )[4 * args.fps : -4 * args.fps]\n",
        "\n",
        "    # noise = 0.001 * th.randn((1, 1, h, 5 * w), device=\"cuda\")\n",
        "    # right_transform = lambda batch: partial(Translate, h=h, w=w, noise=noise)(batch)\n",
        "\n",
        "    # bends += [{\"layer\": t1, \"transform\": right_transform, \"modulation\": right_translation}]\n",
        "    # 3 is decent, kinda abstract spinning, 2 also abstract, \n",
        "    t2 = 10\n",
        "    h2 = 128\n",
        "    w2 = 2 * h2\n",
        "\n",
        "    spin_loop_length = int((spin_end - spin_start) / 7)\n",
        "    energetic_loop_length = int((energetic_end - energetic_start) / 7)\n",
        "\n",
        "    intro_spin = np.zeros(spin_start)\n",
        "    loops_spin = np.concatenate([np.linspace(0, 360, spin_loop_length)] * 7)\n",
        "    outro_spin = np.zeros(energetic_start - spin_end)\n",
        "    loops_energetic = np.concatenate([np.linspace(360, 0, energetic_loop_length)] * 7)\n",
        "    outro_energetic = np.zeros(args.n_frames - energetic_end + 2)\n",
        "    \n",
        "    x_spin = np.concatenate([intro_spin, loops_spin, outro_spin, loops_energetic, outro_energetic])\n",
        "    # print(x_spin, len(x_spin))\n",
        "    # print(\"num frames\", args.n_frames)\n",
        "    spin_translation = (th.tensor(x_spin).float().T)[: args.n_frames]\n",
        "\n",
        "    spin_transform = lambda batch: partial(Rotate, h=h2, w=w2)(batch)\n",
        "\n",
        "    # bends += [{\"layer\": t2, \"transform\": spin_transform, \"modulation\": spin_translation}]\n",
        "\n",
        "    t3 = 12\n",
        "    h3 = 256\n",
        "    w3 = 2 * h3\n",
        "    down_loop_length = int((down_end - down_start) / 3)\n",
        "    up_loop_length = int((up_end - up_start) / 5)\n",
        "    right_loop_length = int((right_end - right_start) / 3)\n",
        "\n",
        "    intro_right = np.zeros(right_start)\n",
        "    loops_right = np.concatenate([np.linspace(0, w3, right_loop_length)] * 3)\n",
        "    outro_right = np.zeros(args.n_frames - right_end + 2)\n",
        "\n",
        "    intro_down = np.zeros(down_start)\n",
        "    loops_down = np.concatenate([np.linspace(0, h3, down_loop_length)] * 3)\n",
        "    loops_up = np.concatenate([np.linspace(0, -h3, up_loop_length)] * 5)\n",
        "    # outro_down = np.zeros(args.n_frames - down_end)\n",
        "    outro_up = np.zeros(args.n_frames - up_end + 2)\n",
        "    \n",
        "    x_right = np.concatenate([intro_right, loops_right, outro_right])\n",
        "    # print(x_right, len(x_right))\n",
        "    # print(\"num frames\", args.n_frames)\n",
        "    x_right += (w3/2.0)\n",
        "    x_right = x_right % (w3*1.0)\n",
        "    # y_down = np.concatenate([intro_down, loops_down, outro_down])\n",
        "    y_down = np.concatenate([intro_down, loops_down, loops_up, outro_up])\n",
        "    # print(y_down, len(y_down))\n",
        "    # print(\"num frames\", args.n_frames)\n",
        "    y_down += (h3/2.0)\n",
        "    y_down = y_down % (h3*1.0)\n",
        "    # x_down = np.zeros(args.n_frames)\n",
        "    # down_translation = (th.tensor([x_down, y_down]).float().T)[: args.n_frames]\n",
        "    down_translation = (th.tensor([x_right, y_down]).float().T)[: args.n_frames]\n",
        "    # down_translation.T[0, down_start - args.fps : down_start + args.fps] = ar.gaussian_filter(\n",
        "    #     down_translation.T[0, down_start - 5 * args.fps : down_start + 5 * args.fps], 5\n",
        "    # )[4 * args.fps : -4 * args.fps]\n",
        "\n",
        "    noise3 = 0.001 * th.randn((1, 1, h3*5, w3*5), device=\"cuda\")\n",
        "\n",
        "    down_transform = lambda batch: partial(TranslateBoth, h=h3, w=w3, noise=noise3)(batch)\n",
        "\n",
        "    # bends += [{\"layer\": t3, \"transform\": down_transform, \"modulation\": down_translation}]\n",
        "\n",
        "    return bends"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2nFx0jYo8Zss"
      },
      "source": [
        "Try to empty GPU memory each time you run generate()\n",
        "\n",
        "Sometimes it doesn't get cleared completely"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B_BM5Sv7vL7A",
        "outputId": "69f4be0e-634d-425b-eccd-a897203eb99d"
      },
      "source": [
        "print(\"Time                     GPU        Used      Total\")\n",
        "!nvidia-smi --query-gpu=timestamp,name,memory.used,memory.free --format=csv,noheader\n",
        "import gc\n",
        "import torch\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()\n",
        "!nvidia-smi --query-gpu=timestamp,name,memory.used,memory.free --format=csv,noheader"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Time                     GPU        Used      Total\n",
            "2021/03/31 00:17:10.669, Tesla P100-PCIE-16GB, 2 MiB, 16278 MiB\n",
            "2021/03/31 00:17:10.878, Tesla P100-PCIE-16GB, 2 MiB, 16278 MiB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mGuZXF8K8uD0"
      },
      "source": [
        "Render the final video!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "celNJVT4LbQA",
        "outputId": "140726ae-efdd-425c-c1bf-ece92d2cf624"
      },
      "source": [
        "from generate_audiovisual import generate\n",
        "# you want to set the batch size and ffmpeg_preset as large and as slow as your GPU can handle\n",
        "# larger batches => faster, slower preset => smaller + better quality video files\n",
        "generate(\n",
        "  ckpt=\"network-snapshot-000384.pt\",\n",
        "  audio_file=\"Song 113.d.wav\",\n",
        "  output_dir=\"../\",\n",
        "  out_size=1920, # at the moment only 512x512, 1024x1024, 1920x1080 outputs are supported (out_size = 512, 1024, or 1920 respectively)\n",
        "  # out_size=1920, # at the moment only 512x512, 1024x1024, 1920x1080 outputs are supported (out_size = 512, 1024, or 1920 respectively)\n",
        "  G_res=1024,\n",
        "  batch=2,  # 4, slow works CUDA out of memory errors => smaller batch (also try running the previous cell, to clear some GPU memory)\n",
        "  ffmpeg_preset=\"medium\",  # RAM crashes => faster preset (see https://trac.ffmpeg.org/wiki/Encode/H.264)\n",
        "  fps=30,\n",
        "  # duration=135, # remove this line for full video\n",
        "  initialize=initialize,\n",
        "  get_latents=get_latents,\n",
        "  get_noise=get_noise,\n",
        "  get_bends=get_bends, # if you're running a 1024px network you can uncomment this for 1920x1080 output\n",
        "  # latent_file=\"my_latents.npy\"\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x864 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "generating latents...\n",
            "chroma:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "[5478, 18, 512] amplitude=0.19866928458213806\n",
            "\n",
            "generating noise...\n",
            "[5478, 1, 4, 8] amplitude=0.5000000596046448\n",
            "[5478, 1, 8, 16] amplitude=0.5\n",
            "[5478, 1, 8, 16] amplitude=0.5000000596046448\n",
            "[5478, 1, 16, 32] amplitude=0.4999999701976776\n",
            "[5478, 1, 16, 32] amplitude=0.5\n",
            "[5478, 1, 32, 64] amplitude=0.5000000596046448\n",
            "[5478, 1, 32, 64] amplitude=0.5\n",
            "[5478, 1, 64, 128] amplitude=0.5000000596046448\n",
            "[5478, 1, 64, 128] amplitude=0.5000000596046448\n",
            "[5478, 1, 128, 256] amplitude=0.5\n",
            "[5478, 1, 128, 256] amplitude=0.5\n",
            "\n",
            "generating network bends...\n",
            "\n",
            "preprocessing took 89.87s\n",
            "\n",
            "rendering 5478 frames...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|███████████████████████████████████████| 5478/5478 [11:04<00:00,  8.24it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "total time taken: 12.76 minutes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ywCHNgfRh0AY"
      },
      "source": [
        "You can now upload a new audio file using the following cell and then render it by only executing the previous cell."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "HNL6fbXThy9M",
        "outputId": "49360168-a446-4aaa-9688-6c1244a0a4e6"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "audio_file = list(uploaded.keys())[0]\n",
        "print('User uploaded file \"{name}\" with length {length} bytes'.format(name=audio_file, length=len(uploaded[audio_file])))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-cc9a3949-8b7c-4bb5-8045-9df2d0062613\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-cc9a3949-8b7c-4bb5-8045-9df2d0062613\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving Song 113.d.wav to Song 113.d.wav\n",
            "User uploaded file \"Song 113.d.wav\" with length 31752044 bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umdCI7DPhtwY"
      },
      "source": [
        "P.S.\n",
        "\n",
        "To load seeds from NVIDIA's official stylegan repositories (or forks thereof):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UI8O_BqNhtUQ"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "seed1 = 728\n",
        "nvlatent1 = np.random.RandomState(seed1).randn(1, 512) # shape = [1, 512]\n",
        "latent1 = np.stack([nvlatent1]*18, axis=1) # shape = [1, 18, 512]\n",
        "\n",
        "seed2 = 797\n",
        "nvlatent2 = np.random.RandomState(seed2).randn(1, 512) # shape = [1, 512]\n",
        "latent2 = np.stack([nvlatent2]*18, axis=1) # shape = [1, 18, 512]\n",
        "\n",
        "seed3 = 731\n",
        "nvlatent3 = np.random.RandomState(seed3).randn(1, 512) # shape = [1, 512]\n",
        "latent3 = np.stack([nvlatent3]*18, axis=1) # shape = [1, 18, 512]\n",
        "\n",
        "seed4 = 709\n",
        "nvlatent4 = np.random.RandomState(seed4).randn(1, 512) # shape = [1, 512]\n",
        "latent4 = np.stack([nvlatent4]*18, axis=1) # shape = [1, 18, 512]\n",
        "\n",
        "seed12 = 736\n",
        "nvlatent12 = np.random.RandomState(seed12).randn(1, 512) # shape = [1, 512]\n",
        "latent12 = np.stack([nvlatent12]*18, axis=1) # shape = [1, 18, 512]\n",
        "\n",
        "seed5 = 760\n",
        "nvlatent5 = np.random.RandomState(seed5).randn(1, 512) # shape = [1, 512]\n",
        "latent5 = np.stack([nvlatent5]*18, axis=1) # shape = [1, 18, 512]\n",
        "\n",
        "seed6 = 716\n",
        "nvlatent6 = np.random.RandomState(seed6).randn(1, 512) # shape = [1, 512]\n",
        "latent6 = np.stack([nvlatent6]*18, axis=1) # shape = [1, 18, 512]\n",
        "\n",
        "seed7 = 740\n",
        "nvlatent7 = np.random.RandomState(seed7).randn(1, 512) # shape = [1, 512]\n",
        "latent7 = np.stack([nvlatent7]*18, axis=1) # shape = [1, 18, 512]\n",
        "\n",
        "seed8 = 710\n",
        "nvlatent8 = np.random.RandomState(seed8).randn(1, 512) # shape = [1, 512]\n",
        "latent8 = np.stack([nvlatent8]*18, axis=1) # shape = [1, 18, 512]\n",
        "\n",
        "seed9 = 738\n",
        "nvlatent9 = np.random.RandomState(seed9).randn(1, 512) # shape = [1, 512]\n",
        "latent9 = np.stack([nvlatent9]*18, axis=1) # shape = [1, 18, 512]\n",
        "\n",
        "seed10 = 719\n",
        "nvlatent10 = np.random.RandomState(seed10).randn(1, 512) # shape = [1, 512]\n",
        "latent10 = np.stack([nvlatent10]*18, axis=1) # shape = [1, 18, 512]\n",
        "\n",
        "seed11 = 733\n",
        "nvlatent11 = np.random.RandomState(seed11).randn(1, 512) # shape = [1, 512]\n",
        "latent11 = np.stack([nvlatent11]*18, axis=1) # shape = [1, 18, 512]\n",
        "\n",
        "\n",
        "latents = np.concatenate([latent1, latent2, latent3, latent4, latent12, latent5, latent6, latent7, latent8, latent9, latent10, latent11], axis=0) # shape = [12, 18, 512]\n",
        "\n",
        "np.save(\"my_latents.npy\", latents)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5mXxC49ehGxE"
      },
      "source": [
        "This assumes a default 1024x1024 network (18 layers) with a latent dimension of 512. You'll need to adjust some numbers above if your network has a different resolution or latent_dim.\n",
        "\n",
        "Now `my_latents.npy` can be loaded to `generate()` as an argument `latent_file=\"my_latents.npy\"`"
      ]
    }
  ]
}